{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f8e3250-7023-4ca2-bec1-74c039b5cd0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24e37424-2465-459d-9057-def5e988a534",
   "metadata": {},
   "outputs": [],
   "source": [
    "dateparse = lambda x: pd.to_datetime(x, format='%Y%m%d', errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0db5f768-9c70-43ef-92c5-2351562b770c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取时间序列数据\n",
    "data = pd.read_csv('000890.csv',parse_dates=['date'], index_col='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8ac4e70f-2828-4477-a2cb-b99fa5e5f9e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化时间序列数据\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(data)\n",
    "plt.title('Time Series Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aaafd4c0-2319-4c8f-9f5f-c26b3e1d3e0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\YFB-SERVER\\AppData\\Local\\miniconda3\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "SARIMAX models require univariate `endog`. Got shape (5829, 5).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# 建立ARIMA模型\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mARIMA\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m      3\u001b[0m model_fit \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit()\n",
      "File \u001b[1;32m~\\AppData\\Local\\miniconda3\\Lib\\site-packages\\statsmodels\\tsa\\arima\\model.py:158\u001b[0m, in \u001b[0;36mARIMA.__init__\u001b[1;34m(self, endog, exog, order, seasonal_order, trend, enforce_stationarity, enforce_invertibility, concentrate_scale, trend_offset, dates, freq, missing, validate_specification)\u001b[0m\n\u001b[0;32m    151\u001b[0m     trend \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    153\u001b[0m \u001b[38;5;66;03m# Construct the specification\u001b[39;00m\n\u001b[0;32m    154\u001b[0m \u001b[38;5;66;03m# (don't pass specific values of enforce stationarity/invertibility,\u001b[39;00m\n\u001b[0;32m    155\u001b[0m \u001b[38;5;66;03m# because we don't actually want to restrict the estimators based on\u001b[39;00m\n\u001b[0;32m    156\u001b[0m \u001b[38;5;66;03m# this criteria. Instead, we'll just make sure that the parameter\u001b[39;00m\n\u001b[0;32m    157\u001b[0m \u001b[38;5;66;03m# estimates from those methods satisfy the criteria.)\u001b[39;00m\n\u001b[1;32m--> 158\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_spec_arima \u001b[38;5;241m=\u001b[39m \u001b[43mSARIMAXSpecification\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    159\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseasonal_order\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseasonal_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    160\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menforce_stationarity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menforce_invertibility\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    161\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconcentrate_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcentrate_scale\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrend_offset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrend_offset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    162\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    163\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidate_specification\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_specification\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    164\u001b[0m exog \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_spec_arima\u001b[38;5;241m.\u001b[39m_model\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39morig_exog\n\u001b[0;32m    166\u001b[0m \u001b[38;5;66;03m# Raise an error if we have a constant in an integrated model\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\miniconda3\\Lib\\site-packages\\statsmodels\\tsa\\arima\\specification.py:454\u001b[0m, in \u001b[0;36mSARIMAXSpecification.__init__\u001b[1;34m(self, endog, exog, order, seasonal_order, ar_order, diff, ma_order, seasonal_ar_order, seasonal_diff, seasonal_ma_order, seasonal_periods, trend, enforce_stationarity, enforce_invertibility, concentrate_scale, trend_offset, dates, freq, missing, validate_specification)\u001b[0m\n\u001b[0;32m    451\u001b[0m \u001b[38;5;66;03m# Validate endog shape\u001b[39;00m\n\u001b[0;32m    452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (validate_specification \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m faux_endog \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    453\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendog\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendog\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m--> 454\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSARIMAX models require univariate `endog`. Got\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    455\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m shape \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendog\u001b[38;5;241m.\u001b[39mshape))\n\u001b[0;32m    457\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_missing \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    458\u001b[0m     \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m faux_endog \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(np\u001b[38;5;241m.\u001b[39misnan(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendog)))\n",
      "\u001b[1;31mValueError\u001b[0m: SARIMAX models require univariate `endog`. Got shape (5829, 5)."
     ]
    }
   ],
   "source": [
    "# 建立ARIMA模型\n",
    "model = ARIMA(data).fit()\n",
    "model_fit = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f01c67d-0a26-46b7-9fe3-e7f436aece19",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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